SmallVectorImpl<Type>* output_types) const;

  // Creates the TF op and also the necessary tfr.cast ops to replace the
  // original TFR call op.
  LogicalResult CreateAndReplaceOp(
      PatternRewriter& rewriter, CallOp call_op,
      const SmallVectorImpl<Type>& output_types,
      const SmallVectorImpl<Value>& inputs, const NamedAttrList& attr_list,

tf_device::ParallelExecuteOp BuildParallelExecuteOp(
    tf_device::ClusterFuncOp cluster_func, OpBuilder* builder) {
  const auto output_types = cluster_func.getResultTypes();
  builder->setInsertionPoint(cluster_func);
  auto parallel_execute = builder->create<tf_device::ParallelExecuteOp>(
      cluster_func.getLoc(), 1, output_types);
  cluster_func->remove();
  auto& block = parallel_execute.GetRegionBlockWithIndex(0);

    %batch_size: i64 {tfr.name="batch_size"},
    %num_parallel_calls: i64 {tfr.name="num_parallel_calls"},
    %drop_remainder: i1 {tfr.name="drop_remainder"},
    %f: !tfr.attr {tfr.name="func"},
    %output_types: !tfr.attr {tfr.name="output_types"},
    %output_shapes: !tfr.attr {tfr.name="output_shapes"},
    %preserve_cardinality: i1 {tfr.name="preserve_cardinality", tfr.default=false}) -> !tfr.tensor {

                                     const StringRef func_name,
                                     const TypeRange output_types,
                                     const ValueRange args) {
  TF::PartitionedCallOp call_op = builder.create<TF::PartitionedCallOp>(
      location, output_types, args,
      FlatSymbolRefAttr::get(builder.getStringAttr(func_name)),
      /*config=*/"", /*config_proto=*/"", /*executor_type=*/"");

      Value dequantized_result = dequantize_op.getInput();
      output_types.push_back(dequantized_result.getType());
      terminator->setOperand(i, dequantized_result);
      returned_op->erase();
    } else {
      output_types.push_back(returned_value.getType());
    }
  }
  auto new_func_type = builder.getFunctionType(input_types, output_types);
  func.setType(new_func_type);
}

enum RemoveVolatileOpsType {

  } : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>

  func.return %0 : tensor<!tf_type.variant>
// CHECK: "tf.MapDataset"(
// CHECK-SAME: <{f = @{{.*}}, metadata = "", output_shapes = [#tf_type.shape<>], output_types = [!tf_type.string], preserve_cardinality = true, use_inter_op_parallelism = true}> {Targuments = []}
}

// CHECK-LABEL: TfTakeWhileDataset

    }

    // Outputs.
    const int num_outputs = launch->output_types().size();
    absl::flat_hash_set<Node*> control_outputs;
    std::vector<std::vector<std::pair<Node*, int>>> data_outputs(num_outputs);
    const DataTypeVector& output_types(launch->output_types());

    for (const Edge* le : launch->out_edges()) {
      if (le->IsControlEdge()) {

      size_of_splits = dim / splits;
      output_shape.push_back(size_of_splits);
    } else {
      output_shape.push_back(dim);
    }
  }

  SmallVector<mlir::Type, 4> output_types;
  for (int i = 0; i < splits; ++i) {
    output_types.push_back(
        mlir::RankedTensorType::get(output_shape, input_type.getElementType()));
  }
  auto size_of_splits_op = Create1DConstantOp(

      shape[split_dimension] = shape[split_dimension] / num_split;
      output_type =
          mlir::RankedTensorType::get(shape, input_type.getElementType());
    }
  } else {
    output_type = input_type;
  }

  // Creates a split op that splits |src_input| along |split_dimension|.
  llvm::SmallVector<mlir::Type, 4> output_types(num_split, output_type);
  *split_op = builder->create<mlir::TF::SplitOp>(

  %iter_handle1 = "tf.IteratorV2"() {container = "c", shared_name = "v0", output_shapes = [#tf_type.shape<>], output_types = [!tf_res]} : () -> !tf_res
  // expected-remark@below {{Result #0, ID 1 : 0, 1}}
  %iter_handle2 = "tf.IteratorV2"() {container = "c", shared_name = "v1", output_shapes = [#tf_type.shape<>], output_types = [!tf_res]} : () -> !tf_res
  func.return
}

// -----